Unlocking Language Models’ Full Potential: Exploring the Stability of MTPrompt

In this article, researchers explore the effectiveness of different types and combinations of task descriptions in improving few-shot text classification. The authors use a method called Meta-Prompt, which combines three types of descriptions: sentence-level, token-level, and text-level descriptions. They find that using more types of descriptions can sometimes lead to confusion when the length of augmented tokens is much longer than the original sentence. To address this issue, they suggest balancing the prompt size and initial input, as well as exploring different representations of task descriptions.
The authors begin by classifying pre-trained language models into three categories: language kernel, token sphere, and text sphere. They explain that the language kernel represents ideal language syntax, while the token sphere contains words with different positions but similar semantics (like "apple" and "fruit"). The text sphere is used to represent different sentences, with positions calculated based on the words in the token sphere.
The authors then explore the effectiveness of various combinations of task descriptions using Meta-Prompt. They find that the combination of sentence-level, token-level, and text-level descriptions achieves the highest accuracy, but the variance increases as more types of descriptions are combined. To improve this, they suggest balancing prompt size and initial input, as well as exploring different representations of task descriptions.
Overall, the article provides insights into the complex relationship between task descriptions and few-shot text classification, highlighting the importance of finding the right balance between prompt size, input length, and representation of task descriptions. By using everyday language and engaging metaphors or analogies, the authors make the concepts more accessible and easier to understand for an average adult reader.